Chapter 2 preliminary model results
Cameron Ho
2025-12-15
Modeling
I am using a conditional binomial model to determine what factors
impact movement decisions of territorial common ravens during the winter
in Yellowstone. I will look at if various predictors impact if a raven
decided to leaves its territory, and if it did, if it visit the Gardiner
hunting region or not. The largest predictors here revolve around food
availability of carrion created by human hunting activity and
wolves.
There are two data sets, one for each part of the conditional
model. The first one includes all of the days during the Yellowstone
Wolf Project winter study periods (Nov 15- Dec 15 & Mar 1 - Mar 30)
because my main predictor is the presence of wolf kills within a ravens
territory and the carcass data is most reliable for that period of time
due to GPS collar fix rates.
The second data set includes all winter from the start of the hunting
season (varies by year, but always in the last week of October) to the
end of March (end of late winter study). I can include these other
periods because I am no longer considering wolf kills as a predictor
because the raven at this point in the conditional model has already
decided to leave its territory. This also allows me to have more data on
days with low or no hunter gutpile availability from January and
February.
For both data sets, all data points are excluded if that raven had less
than 5 GPS points and the movement decisions was the negative for that
binomial model (didn’t leaves its territory or didn’t visit the hunting
area).
## dataset for part 1 of conditional model
ws_model_data <- read_csv(here("data", "clean", "commute_data.csv")) %>%
#restricting to only winter study months
filter((paste(month, day, sep = "-") >= "11-15" &
paste(month, day, sep = "-") <= "12-15") |
(paste(month, day, sep = "-") >= "3-1" &
paste(month, day, sep = "-") <= "3-30")) %>%
#removing days when there is less than 5 GPS point
#unless the result is Jardine
filter(!(n_point < 5 & terr_bin == F)) %>%
#only columns used in model
dplyr::select(terr_bin, raven_id, rf_active_kill, final_take_bms1, final_take,
hunt_season, rf_avg_terr_kill_density, dist2nentrance,
study_period, temp_max, snow_depth, prop_group_left_terr) %>%
#making sure rows are complete
filter(complete.cases(.))
## dataset for part 2 of conditional model
hunt_model_data <- read_csv(here("data", "clean", "commute_data.csv")) %>%
#only have days ravens decided to leave territory
filter(terr_bin == 1) %>%
#removing days when there is less than 5 GPS point
#unless the result is Jardine
filter(!(n_point < 5 & hunt_bin == F)) %>%
#only columns used in model
dplyr::select(hunt_bin, raven_id, final_take_bms1, final_take, hunt_season,
dist2nentrance, study_period, temp_max, snow_depth, prop_group_visit_hunt) %>%
#making sure rows are complete
filter(complete.cases(.)) Here is a description of all the model covariates - rf_active_kill:
if a kill detected through the RF predictive model is present on the
ravens territory or not - final_take_bms1: the estimated available
biomass from human hunting adjusted for relative weights of various
species (the multiplier for each is 0.35*deer, 1*elk, 2.15*bison). The
biomass from the previous day is rolled over at 0.25x rate -
hunt_season: if it is currently within the hunting season or not. The
hunting season for MTFWP is defined by policy. The bison hunting season
starts when bison are regularly taken from the surveys done by the Bison
Project since that hunt’s timing is dictated by weather and
migration
- rf_avg_terr_kill_density: the average number of kills per 30 days
during early or late winter study period - dist2nentrance: distance from
raven territory to the north entrance station - study_period: early or
late winter study - temp_max: daily maximum temperature - snow_depth:
daily snow depth prop_group_left_terr: the proportion of other ravens
that left their territory prop_group_visit_hunt: the proportion of other
ravens that visited the Gardiner hunting region ## Part 1 of conditional
model The first part of the conditional model predicts if a raven will
leaves it territory or not.
DEPENDENT VARIABLE
terr_bin
1 = left territory
0 = stayed on territory
I am reporting the results from two different models that use a
different covariate to represent the hunting availability. The first on
is the hunting biomass, which is a more fine scale daily estimate of
hunter take. This relies on elk movements tracked using GPS collars to
add variation to the MTFWP hunting season. The bison take estimate
depends on daily surveys performed by the NPS Bison Project. I report
both of these models because the biomass covariate has so much
uncertainty in how it is calculated during the MTFWP season since it
doesn’t actually quantify the number of daily kills. It tries to account
for daily variation in take by using elk movement to proxy how many
individuals are available to be taken. It also includes a multiplier for
the relative biomass (numbers in covariate explanation above) that has
elk as the baseline and deer and bison multiplied to be greater or less
than elk. It is very possible that a reader doesn’t like or believe it
is calculated correctly. By also including the hunting season model we
can show that the overall hunting period itself, regardless of the daily
biomass availability, has an effect on decision making. Then, we can say
that we tried to get finer scaled using the biomass covariate. The
results are similar enough that it is believable and doesn’t drastically
change interpretation, but a reader can disregard that part if they
want.
Another idea is that I can go for something inbetween and at least
remove the relative weight of the animals from the calculation and just
have the estimated take number. It seems like this leads to pretty
similar results, though it won’t be shown here.
1. bms_window: The
most specific is the a measure of the available biomass.
mod_terr_bms <- glmer(terr_bin ~ (1|raven_id) + rf_active_kill * scale(final_take_bms1) + scale(rf_avg_terr_kill_density) +
scale(dist2nentrance) + study_period * scale(temp_max) + scale(snow_depth) + scale(prop_group_left_terr),
data = ws_model_data,
family = "binomial",
nAGQ = 40,
control = cntrl)
summary(mod_terr_bms)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 40) [glmerMod]
## Family: binomial ( logit )
## Formula: terr_bin ~ (1 | raven_id) + rf_active_kill * scale(final_take_bms1) +
## scale(rf_avg_terr_kill_density) + scale(dist2nentrance) +
## study_period * scale(temp_max) + scale(snow_depth) + scale(prop_group_left_terr)
## Data: ws_model_data
## Control: cntrl
##
## AIC BIC logLik -2*log(L) df.resid
## 1140.4 1206.6 -558.2 1116.4 1816
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.9552 0.1341 0.2184 0.3691 3.4784
##
## Random effects:
## Groups Name Variance Std.Dev.
## raven_id (Intercept) 2.209 1.486
## Number of obs: 1828, groups: raven_id, 20
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.83214 0.39763 7.123 1.06e-12
## rf_active_killTRUE -0.84508 0.40682 -2.077 0.0378
## scale(final_take_bms1) -0.01926 0.07886 -0.244 0.8070
## scale(rf_avg_terr_kill_density) 0.40922 0.40247 1.017 0.3093
## scale(dist2nentrance) -0.30534 0.37862 -0.806 0.4200
## study_periodlate -0.42413 0.20649 -2.054 0.0400
## scale(temp_max) -0.18614 0.11358 -1.639 0.1012
## scale(snow_depth) 0.14586 0.10797 1.351 0.1767
## scale(prop_group_left_terr) 0.14669 0.08473 1.731 0.0834
## rf_active_killTRUE:scale(final_take_bms1) 0.30192 0.56954 0.530 0.5960
## study_periodlate:scale(temp_max) 0.09580 0.16502 0.581 0.5615
##
## (Intercept) ***
## rf_active_killTRUE *
## scale(final_take_bms1)
## scale(rf_avg_terr_kill_density)
## scale(dist2nentrance)
## study_periodlate *
## scale(temp_max)
## scale(snow_depth)
## scale(prop_group_left_terr) .
## rf_active_killTRUE:scale(final_take_bms1)
## study_periodlate:scale(temp_max)
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) rf__TRUE s(__1) s(____ scl(2) stdy_p scl(t_) scl(s_) s(___)
## rf_ctv_TRUE -0.016
## scl(fnl__1) -0.043 0.006
## scl(rf____) 0.173 -0.015 0.002
## scl(dst2nn) 0.075 0.023 0.001 0.082
## study_prdlt -0.297 -0.036 0.127 0.010 0.010
## scl(tmp_mx) 0.019 -0.050 -0.078 -0.009 0.009 -0.086
## scl(snw_dp) 0.154 -0.052 -0.184 0.009 0.018 -0.568 0.189
## scl(prp___) -0.051 0.010 -0.011 0.026 0.014 0.258 0.020 -0.189
## r__TRUE:(__ -0.033 0.421 -0.101 -0.014 0.005 0.106 -0.048 -0.132 0.003
## stdy_pr:(_) 0.013 0.056 0.094 0.011 0.002 -0.124 -0.649 0.078 0.026
## r__TRUE:
## rf_ctv_TRUE
## scl(fnl__1)
## scl(rf____)
## scl(dst2nn)
## study_prdlt
## scl(tmp_mx)
## scl(snw_dp)
## scl(prp___)
## r__TRUE:(__
## stdy_pr:(_) -0.028- hunt_season: The least specific is just whether it is within the
hunting season.
One massive thing to note here is that i have removed the interaction effect. When I updated the data with the bison specific polygon and the bison hunting start date, it made standard error for the interaction 160. That’s pretty terrible.
mod_terr_hseason <- glmer(terr_bin ~ (1|raven_id) + rf_active_kill + hunt_season + scale(rf_avg_terr_kill_density) +
scale(dist2nentrance) + study_period * scale(temp_max) + scale(snow_depth) + scale(prop_group_left_terr),
data = ws_model_data,
family = "binomial",
nAGQ = 40,
control = cntrl)
summary(mod_terr_hseason)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 40) [glmerMod]
## Family: binomial ( logit )
## Formula:
## terr_bin ~ (1 | raven_id) + rf_active_kill + hunt_season + scale(rf_avg_terr_kill_density) +
## scale(dist2nentrance) + study_period * scale(temp_max) +
## scale(snow_depth) + scale(prop_group_left_terr)
## Data: ws_model_data
## Control: cntrl
##
## AIC BIC logLik -2*log(L) df.resid
## 1133.8 1194.4 -555.9 1111.8 1817
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.8293 0.1303 0.2201 0.3719 2.6237
##
## Random effects:
## Groups Name Variance Std.Dev.
## raven_id (Intercept) 2.09 1.446
## Number of obs: 1828, groups: raven_id, 20
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.14632 0.47915 4.479 7.48e-06 ***
## rf_active_killTRUE -0.93531 0.36867 -2.537 0.0112 *
## hunt_seasonTRUE 0.83539 0.35862 2.329 0.0198 *
## scale(rf_avg_terr_kill_density) 0.38958 0.39292 0.992 0.3214
## scale(dist2nentrance) -0.34419 0.37016 -0.930 0.3524
## study_periodlate -0.53459 0.20840 -2.565 0.0103 *
## scale(temp_max) -0.23303 0.11688 -1.994 0.0462 *
## scale(snow_depth) 0.17562 0.10575 1.661 0.0968 .
## scale(prop_group_left_terr) 0.13329 0.08569 1.555 0.1198
## study_periodlate:scale(temp_max) 0.13496 0.16540 0.816 0.4145
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) r__TRU h_TRUE s(____ scl(2) stdy_p scl(t_) scl(s_) s(___)
## rf_ctv_TRUE 0.008
## hnt_ssnTRUE -0.585 -0.015
## scl(rf____) 0.150 -0.011 -0.021
## scl(dst2nn) 0.089 0.025 -0.052 0.088
## study_prdlt -0.083 -0.086 -0.260 0.020 0.026
## scl(tmp_mx) 0.139 -0.028 -0.217 -0.005 0.019 -0.009
## scl(snw_dp) 0.044 0.002 0.127 0.001 0.008 -0.541 0.150
## scl(prp___) 0.001 0.013 -0.073 0.027 0.017 0.274 0.028 -0.206
## stdy_pr:(_) -0.055 0.067 0.116 0.009 -0.004 -0.157 -0.661 0.093 0.021Model AIC comparisons.
AIC(mod_terr_bms)## [1] 1140.428AIC(mod_terr_hseason) #better## [1] 1133.787It seems like it does matter what hunting covariate I use. The ravens
did respond positively to the overall hunting season, but not the
relative available hunter biomass. While the direction of the effects is
the same, there was no evidence that the actually amount of biomass
mattered when deciding whether to leave their territories or now. It
seem more important that there may be biomass available at all.
Following predictions, the active kill covariate is important with an
active kill within 1 km of their territory reducing the chances of the
raven deciding to leave.
The average kill density in a ravens territory did not impact movement
decisions. So they don’t consider the past or prior knowledge of their
territory and are only responding to more immediate stimuli. This makes
sense when considering that a high productivity territory is still
unlikely to have a kill made each day. Then their response to no
immediate kills in their territory is to leave, which is still a gamble
since you don’t know what the food situation will be like in Gardiner or
elsewhere; however, in most cases those human sites will still be less
of a gamble than a raven in a high productivity territory.
The study period being early or late winter has a negative effect in the
late winter in both models, which follows my prediction about how
pre-breeding behaviors would impact raven movement by keeping them on
territory more often.
The maximum daily temperature had a negative effect in both models.
Higher temperatures lowered the chance of a raven leaving its territory.
A good reason for this might be that the colder days require more energy
to maintain body temperature, so a more guaranteed food sources is
taken. Then on warmer days when temperature regulation is easier, they
don’t risk as much by staying on their territory. An interaction between
temperature and study period was tested to tell if ravens were
influenced by temperature differently at different times of year and the
effect was insignificant and is not included here.
Snow depth had the same effect found from the Walker et al. study that
greater snow depth increased the chances of the raven being in
anthropogenic hunting areas (leaving their territory). This isn’t a
perfect conclusion though because even though they leave their
territory, they could still be staying in the national park. However, it
does agree with the alternative logic that higher snow depth leads to
ungulate migration and more potential hunting availability. So this
might be a cue that they are not following or attempting to gain
knowledge about actual hunting availability (hence a lack of effect for
the actual hunting covariate) and are instead following proxies for it.
snow depth and the hunting biomass covariates have a high correlation in
the model output. This is likely because the biomass calculation takes
into account elk migration timing, which is heavily influenced by snow
depth.
One super interesting thing to note is that I ran this model with the
group travel covariate before adding the temperature, and the group
travel stopped being significant after the addition of the temperature.
So it would seem like they don’t take into account the other ravens that
much and instead are all responding to the same stimuli.
Here is the bootstrapped parameter confidence intervals for model 1
using the biomass estimate as the hunting covariate.
Part 2 of conditional model
The second part of the conditional model predicts if, when a raven
left its territory, if it visited the Gardiner hunting region.
DEPENDENT VARIABLE
hunt_bin
1 = visited hunting
0 = visited other place
Again, I am looking at models with both hunting covariates.
However, this time I removed the wolf kill and study period
covariates.
I don’t consider wolf kills anymore because, at this point in the model,
the raven has already left its territory. It is true that a raven could
travel to a wolf kill outside of its territory, but I am interested in
the decision to travel to the Gardiner hunting region, so those other
options don’t matter as much. That includes other cities and towns. This
part of the conditional model acknowledges their presence, but isn’t
really interested in them specifically.
I don’t consider study period anymore because, again, in this part of
the conditional model the raven has already decided to leave its
territory. The breeding behavior hypothesis was that ravens would want
to stay on their territory more during late winter to
perform breeding behaviors. This doesn’t matter anymore here.
1. bms_window: The most specific is the a measure of the available
biomass.
mod_hunt_bms <- glmer(hunt_bin ~ (1|raven_id) + scale(final_take_bms1) + scale(dist2nentrance) +
scale(prop_group_visit_hunt) + scale(temp_max) + scale(snow_depth),
data = hunt_model_data,
family = "binomial",
nAGQ = 40,
control = cntrl)
summary(mod_hunt_bms)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 40) [glmerMod]
## Family: binomial ( logit )
## Formula:
## hunt_bin ~ (1 | raven_id) + scale(final_take_bms1) + scale(dist2nentrance) +
## scale(prop_group_visit_hunt) + scale(temp_max) + scale(snow_depth)
## Data: hunt_model_data
## Control: cntrl
##
## AIC BIC logLik -2*log(L) df.resid
## 3591.1 3634.9 -1788.6 3577.1 3821
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6969 -0.5656 0.2976 0.5821 4.2882
##
## Random effects:
## Groups Name Variance Std.Dev.
## raven_id (Intercept) 3.814 1.953
## Number of obs: 3828, groups: raven_id, 20
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.07973 0.46817 0.170 0.864768
## scale(final_take_bms1) 0.11913 0.04823 2.470 0.013519 *
## scale(dist2nentrance) -1.83095 0.49636 -3.689 0.000225 ***
## scale(prop_group_visit_hunt) 0.57956 0.04824 12.014 < 2e-16 ***
## scale(temp_max) -0.15944 0.04523 -3.525 0.000424 ***
## scale(snow_depth) 0.05159 0.04819 1.070 0.284397
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) s(__1) scl(2) s(___) scl(t_)
## scl(fnl__1) 0.001
## scl(dst2nn) 0.094 -0.002
## scl(prp___) -0.004 -0.128 -0.018
## scl(tmp_mx) -0.011 -0.091 0.009 0.080
## scl(snw_dp) -0.013 -0.117 -0.003 -0.017 0.381- hunt_season: The least specific is just whether it is within the
hunting season
There is no issues with inflated standard errors here because this model does not include any of the wolf kill covariates.
mod_hunt_hseason <- glmer(hunt_bin ~ (1|raven_id) + hunt_season + scale(dist2nentrance) +
scale(prop_group_visit_hunt) + scale(temp_max) + scale(snow_depth),
data = hunt_model_data,
family = "binomial",
nAGQ = 40,
control = cntrl)
summary(mod_hunt_hseason)## Generalized linear mixed model fit by maximum likelihood (Adaptive
## Gauss-Hermite Quadrature, nAGQ = 40) [glmerMod]
## Family: binomial ( logit )
## Formula: hunt_bin ~ (1 | raven_id) + hunt_season + scale(dist2nentrance) +
## scale(prop_group_visit_hunt) + scale(temp_max) + scale(snow_depth)
## Data: hunt_model_data
## Control: cntrl
##
## AIC BIC logLik -2*log(L) df.resid
## 3548.1 3591.8 -1767.0 3534.1 3821
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.9848 -0.5476 0.2841 0.5674 4.7828
##
## Random effects:
## Groups Name Variance Std.Dev.
## raven_id (Intercept) 3.735 1.933
## Number of obs: 3828, groups: raven_id, 20
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.48378 0.47029 -1.029 0.303629
## hunt_seasonTRUE 0.77755 0.11122 6.991 2.73e-12 ***
## scale(dist2nentrance) -1.85464 0.49155 -3.773 0.000161 ***
## scale(prop_group_visit_hunt) 0.54532 0.04896 11.139 < 2e-16 ***
## scale(temp_max) -0.17626 0.04580 -3.849 0.000119 ***
## scale(snow_depth) 0.16720 0.05109 3.273 0.001066 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) h_TRUE scl(2) s(___) scl(t_)
## hnt_ssnTRUE -0.169
## scl(dst2nn) 0.094 -0.015
## scl(prp___) 0.017 -0.126 -0.017
## scl(tmp_mx) 0.004 -0.090 0.008 0.090
## scl(snw_dp) -0.061 0.299 -0.010 -0.053 0.331Model AIC comparison.
AIC(mod_hunt_bms)## [1] 3591.147AIC(mod_hunt_hseason) #better## [1] 3548.087In both of the models the hunting covariate was significant and
followed the trend of more hunting, more likely the raven is to visit
the hunting area after it has decided to leave its territory.
The distance to the hunting area was also always significant with ravens
holding territories closer to the hunting area visiting more frequently.
Makes sense that the less time and energy it takes to get there makes it
more worth checking out the area. For some ravens that are really close,
it also gives them the advantage of potentially being able to hear
gunshots that indicate potential hunter success.
The proportion of other ravens that are visiting the hunting area has a
positive effect. When considered in conjunction with the effect of the
group covariate in part 1 of the conditional model: ravens aren’t
influenced by other ravens when deciding to leave their territory;
however, if they do choose to leave they are often visiting the same
destinations as the rest of the ravens that leave their territories. So
if they choose to leave, which is a decision based on abiotic factors
like weather and available local food, they then utilize social cues and
shared information.
The maximum daily temperature had a negative effect in both models,
lowering the chance of a raven visiting the hunting area if it decided
to leave its territory during warmer weather.
The effect of snow depth is more uncertain from these models since only
model 1 found a significant effect and the other model was not
significant. The effect direction was the same though and matches with
the effect found by Walker et al.
Here is the bootstrapped parameter confidence intervals for model 1
using the biomass estimate as the hunting covariate.
## Figures Here is a table that shows the number of times raven made
each decision. Beth had the idea to make some kind of heat map similar
to the map below that has individual examples of ravens GPS points, but
for raven decisions. This would be so there was some, more easily
understandable (the giant mass of GPS points is hard to comprehend and
compare), visual example. The Gardiner hunting region and the territory
would be heat map areas and then the times that the raven left its
territory but didn’t go to Gardiner would leave the GPS points like map
1.B. This would mean that you could only have 1 raven per map so that
the heat map reflected that individuals decisions.
(raven_decisions <- read_csv(here("data", "clean", "commute_data.csv")) %>%
group_by(raven_id) %>%
summarize(terr = sum(terr_bin == FALSE),
other = sum(terr_bin == TRUE & hunt_bin == FALSE),
hunt = sum(hunt_bin == TRUE)) %>%
# adding column for total sample size for each raven
mutate(n = hunt + other + terr))## # A tibble: 20 × 5
## raven_id terr other hunt n
## <chr> <int> <int> <int> <int>
## 1 7484 42 8 64 114
## 2 7485 108 220 0 328
## 3 7489 27 155 275 457
## 4 7489_2 4 24 14 42
## 5 7490_3 21 84 239 344
## 6 7492 69 144 325 538
## 7 7493_2 62 48 1 111
## 8 7494 105 184 0 289
## 9 7495 14 19 115 148
## 10 7530 148 50 441 639
## 11 7531 24 81 116 221
## 12 7561 58 194 189 441
## 13 7565 35 111 9 155
## 14 7595 36 160 149 345
## 15 7640_3 20 69 289 378
## 16 7652_2 5 113 74 192
## 17 7665 41 255 148 444
## 18 7672 13 88 46 147
## 19 8900 0 37 77 114
## 20 8902 0 3 1 4But I think an even better option would be a bar chart with 3 levels
showing the proportion of days making whatever decisions (similar to how
we handled the raven foraging).
Here is an visual example of three different ravens with
territories at different distances from the Gardiner hunting region area
and their GPS points.
Of the three ravens, the orange raven with its territory at Old Faithful
is the only one that doesn’t visit the hunting area which is why it
looks the same in both maps. Instead, its trips are consistently to the
closer town of West Yellowstone, presumably to feed on human refuse. It
probably makes this decision because the time investment to travel up to
the Gardiner hunting region is not worth it when an alternative location
with consistent foraging is nearby.
Even though purple is a similar distance to West Yellowstone where
orange visits frequently, it chooses to travel to the north instead. It
probably makes this choice because unlike orange, the time investment is
about the same between the two locations. And Gardiner has a much higher
level of hunting offering a potentially better foraging opportunity
while also have human refuse as a back up. Almost all trips north for
purple resulted in a visit to the Gardiner hunting region, and the few
remaining points up north are probably because of a missed fix in the
hunting area. Most of the time when the purple chooses to leave its
territory, but not visit the Gardiner hunting region, it is actually
still hanging out around its territory but just outside of the 1
kilometer around the 90% minimum convex polygon I am currently
considering their territory.
Green’s territory is so close to the Gardiner hunting region that it
only makes sense that it visits so often, given that the flight time for
that trip is less than 30 minutes. On days that it leaves its territory
but does not visit the Gardiner hunting region it visits various areas
in the northern range inside the park and even ventures eastward a few
times during October before it gets too cold.
One super interesting thing to note is that you would think that there
would be more obvious instances of ravens visiting wolf kills outside
their territories which would show up as small concentrations of GPS
points in map B. However, this almost never occurs, or at least isn’t
obvious. I think this is because either the carcass is close enough to
their territory that I still consider the raven as having not left. Or,
ravens will frequently visit wolf kills outside their territory,
followed by a trip to the Gardiner hunting area. This second explanation
actually lines up with some of my experiences seeing a group of ravens
fly into a carcass sometime in the morning, only to leave not long
after. I look into this a farther down using some tables.
Map A shows the all GPS points made by each of the three ravens. Map B
shows the GPS points for the three ravens when they choose to exit their
territories (highlighted in red), but did not visit the Gardiner hunting
region.
Here is a heat map of the
raven GPS points outside the park from November and March along with a
polygon denoting the area used to determine if a raven attempted to
search for hunter created resources during the MTFWP rifle hunting
season and the tribal bison hunt. There is a separate polygon for the
MTFWP rifle hunting season and the tribal bison hunt. The MTFWP hunting
region is defined as the space 5 km from all roads within 10 km north of
the national park boundary. This space has a high density of hunting
activity in close proximity to the northern range of the park. The 5 km
buffer around roads was used as a reasonable distance for a hunter to
travel. There is certainly successful hunting attempts in the areas
outside this polygon, but the relative number of those will be low
compared to hunting within the polygon. The bison hunting region is much
smaller because of bison movement and hunter effort. The bison are
restricted in their movement and rarely, if ever, travel beyond the
cattle grate that is place at the south end of Yankee Jim canyon. Bison
hunting happens almost exclusively along the road, since the main
migration path of the bison out of the park is funneled along the
roadway at Beattie Gulch. Plus, if someone did successfully harvest a
bison in the backcountry, that hike out would be awful, even with a
large group due to the size of bison. This polygon does include the town
of Gardiner along with the local landfill and water treatment ponds that
ravens have been documented using as locations to forage since I made
the assumption that a dominant territorial raven visiting this area
would always attempt to find hunter offal, even if unsuccessful. 26% of
trips to the Gardiner hunting region during the hunting season resulted
in at least 1 GPS point at the landfill and water treatment pond.
Here is a map of the relevant
polygons used including the study area of Yellowstone National Park, the
northern range of Yellowstone, and raven territories calculated using
90% minimum convex polygons of their breeding season GPS data. All raven
used for this study have territories within the boundary of the national
park. In 2 cases where a territorial pair had both individuals GPS
tagged, only the female was used since the GPS fix rate was higher due
to better battery levels resulting from less coverage of the solar panel
during the winter.
## Wolf kill visits I looked at how often
ravens were visiting wolf kills when they left their territory because
it looked like they didn’t visit kills that often when they left their
territory but didn’t visit the Gardiner hunting region (map 1.B). This
currently includes both the RF predicted wolf kills and the wolf project
kill database to have the maximum coverage of possible kills.
source(here("scripts", "exploratory", "wolf_kill_visits.R"))## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)# table for days visiting wolf kills with no hunting visit
leave_no_hunt_wolf_kills## # A tibble: 11 × 4
## raven_id days_visit total_days prop_visit
## <chr> <int> <int> <dbl>
## 1 7490_3 6 27 0.222
## 2 7492 3 63 0.0476
## 3 7531 7 39 0.179
## 4 7561 21 133 0.158
## 5 7595 13 126 0.103
## 6 7640_3 6 36 0.167
## 7 7652_2 1 47 0.0213
## 8 7665 11 171 0.0643
## 9 7672 1 49 0.0204
## 10 8900 1 12 0.0833
## 11 8902 1 3 0.333# table for days visiting wolf kills with hunting visit
hunt_wolf_kills## # A tibble: 16 × 4
## raven_id days_visit total_days prop_visit
## <chr> <int> <int> <dbl>
## 1 7484 11 63 0.175
## 2 7489 16 257 0.0623
## 3 7490_3 11 152 0.0724
## 4 7492 17 248 0.0685
## 5 7495 4 108 0.0370
## 6 7530 20 378 0.0529
## 7 7531 15 100 0.15
## 8 7561 9 142 0.0634
## 9 7565 3 6 0.5
## 10 7595 7 132 0.0530
## 11 7640_3 6 176 0.0341
## 12 7652_2 10 49 0.204
## 13 7665 9 125 0.072
## 14 7672 6 32 0.188
## 15 8900 8 33 0.242
## 16 8902 1 1 1It looks like wolf kill visits are not a large portion of trips
outside of the territory, regardless of if the raven ends up in the
Gardiner hunting region or not. Although it is more than map 1.B makes
it look like. This might be because they don’t actually spend a lot of
time at these kills, so they don’t accumulate GPS points there.
This tracks with the foraging paper and the relative importance of
natural carrion compared to anthropogenic resources used by most ravens.
It is worth noting that a fair number of times ravens do visit wolf
kills before preceding to the Gardiner hunting region to search for
hunter gutpiles.